With the rapid development of intelligent manufacturing industries, big data analysis and application has become one of key indices for improving the production power, the competitive power, and the innovation power of manufacturing industries. In intelligent manufacturing industries, the capability of effectively analyzing big data and performing production dispatching according to the analysis results not only enables rapid application in various high-tech manufacturing industries (e.g., photoelectric semiconductors) or conventional manufacturing industries, but also can assist the manufacturing industries in reducing the operation and maintenance cost and improving the resource utilization efficiency of factories.
A dispatching method in the conventional manufacturing industries performs dispatching mainly by analyzing similar product orders in a historical database or establishing various kinds of production data models. However, unlike the technology described in the present invention, the conventional dispatching technology does not record multiple levels of steady state production rates of a product in each working bench through different data binning techniques and perform dispatching by comparing multiple levels of steady state production rates of each of a plurality of to-be-produced products and/or a plurality of products in production in the whole factory. Therefore, the conventional technology is likely to be influenced by abnormal data in the historical database and thus cannot provide a more effective, accurate and instant dispatching result.
Accordingly, an urgent need exists in the art to provide a dispatching technology which optimizes the efficiency of the working bench by mastering correlations between the production efficiency of the product on the working bench and the category, product characteristics and materials of the working bench.